Senior AI / ML Production Engineer
Confirmed on the employer's own hiring board on Nov 11, 2026. First seen by Alion on Aug 31, 2026. Alldus scores E on the Alion truth index.
Our client, a global life sciences organisation, is hiring a Senior AI/ML Production Engineer to joint heir team remotely on a 12-month daily rate contract. The successful candidate will take machine learning models from prototype to production, developing the services, pipelines and infrastructure needed to run reliable, scalable AI applications in clinical research.
Responsibilities
Turn Python prototypes and machine learning models into maintainable production services.
Develop and optimise Python applications, REST APIs, microservices and automated data pipelines.
Build CI/CD and MLOps pipelines for testing, deployment, model versioning, monitoring and reproducibility.
Improve code performance, memory efficiency and reliability when processing large, complex datasets.
Deploy AI/ML solutions on AWS and/or Azure using Docker, Kubernetes and cloud-native services.
Implement logging, monitoring and model performance tracking, and troubleshoot production issues.
Work with Data Science and Engineering teams to define deployment requirements and resolve technical challenges.
Apply secure engineering practices and support relevant data privacy and regulatory requirements.
Skillset
At least 5 years of professional software engineering experience, including deploying and supporting AI/ML solutions in production.
Strong Python skills, with experience writing maintainable, well-tested and efficient code.
A track record of taking machine learning models or analytical applications from prototype to production.
Experience building APIs and backend services using FastAPI, Flask or similar frameworks.
Practical experience with MLOps, CI/CD and automated deployment, using tools such as MLflow, Kubeflow, Airflow, DVC or Databricks.
Experience with AWS and/or Azure, alongside Docker and Kubernetes.
An understanding of model lifecycle management, monitoring and production troubleshooting.
Experience with SQL databases, data pipelines and large-scale data processing.
Familiarity with Git, automated testing, code reviews and infrastructure automation.